Predictive modeling of CO solubility in piperazine aqueous solutions using boosting algorithms for carbon capture goals.

Carbon dioxide (CO) is the main greenhouse gas that drives global warming, climate change, and other environmental issues. CO absorption using amine solvents stands out as one of the most well-known industrial technologies of CO capture. However, accurate prediction of CO absorption in aqueous amine solutions under different operating conditions is crucial for designing an efficient amine scrubbing system in power plants. In this work, CO solubility in aqueous piperazine (PZ) solutions was modeled using 517 experimental data points covering a temperature range of 298 to 373 K, PZ concentration of 0.1 to 6.2 mol/L (M), and CO partial pressure of 0.03 to 7399 kPa. To this end, four robust machine learning algorithms, including gradient boosting with categorical features support (CatBoost), light gradient boosting machine (LightGBM), extreme gradient boosting (XGBoost), and adaptive boosting decision trees (AdaBoost-DT) were utilized. Among the developed models, the CatBoost model presented the highest accuracy with an overall determination coefficient (R) of 0.9953 and an average absolute relative error of 2.36%. Sensitivity analysis revealed that CO partial pressure had the greatest influence on CO absorption in aqueous PZ solutions, followed by PZ concentration and temperature. Moreover, CO partial pressure positively influenced CO absorption in aqueous PZ solutions, while PZ concentration and temperature exhibited negative effects. Finally, the leverage technique indicated that both the experimental data bank used for modeling and the model's estimates were statistically acceptable and valid showing only 8 points (∼1.5% of total data) as possible suspected data.